In recent months, the Massachusetts Institute of Technology (MIT) called for papers on the topic of artificial intelligence (AI), set to construct effective roadmaps, policy recommendations, and action strategies across generative AI. The response was overwhelmingly positive with 75 paper proposals submitted. Out of these, 27 were chosen for seed funding. Given the successful outcome of the first call for papers, MIT President Sally Kornbluth and Provost Cynthia Barnhart initiated a second call for anonymously blinded reviewed proposals in the fall. This generated another 53 submissions.
For this second round, a faculty committee reviewed and selected 16 proposals. These proposals were written by interdisciplinary teams of faculty and researchers affiliated with all five MIT schools and the MIT Schwarzman College of Computing. The exploratory funding received will range between $50,000 and $70,000 for each group to create 10-page impact papers. These papers will be shared publicly via MIT Open Publishing Services program managed by MIT Press.
A variety of topics across different sectors are encompassed in the chosen proposals, illustrating the extensive potential of applying generative AI. The proposed subjects range from the role of generative AI in live music performances to its impact on the creative economy. Other themes include the use of AI in drug discovery, supporting the aging population, and utilizing AI for civic engagement in cities. In addition, a few proposals deal with issues concerning privacy and the verifiability of generative AI.
Thomas Tull, a member of the MIT School of Engineering Dean’s Advisory Council, contributed funding for this initiative, continuing his support from the first round of papers. The backers believe the response and the ideas generated from the calls for papers indicate an increasing interest and recognition of the potential of AI to transform various aspects of society. These papers are hence believed to catalyze the development and implementation of effective and efficient AI applications across different domains.
